问如何调试LSQ子问题中的故障

EN

import openmdao.api as om
import dymos as dm
from dymos.examples.plotting import plot_results
from dymos.examples.brachistochrone import BrachistochroneODE
import matplotlib.pyplot as plt

#
# Initialize the Problem and the optimization driver
#
p = om.Problem(model=om.Group())
p.driver = om.ScipyOptimizeDriver()
# p.driver.options['tol'] = 1e-9
p.driver.declare_coloring()

#
# Create a trajectory and add a phase to it
#
traj = p.model.add_subsystem('traj', dm.Trajectory())

phase = traj.add_phase('phase0',
                       dm.Phase(ode_class=BrachistochroneODE,
                                transcription=dm.GaussLobatto(num_segments=10)))

#
# Set the variables
#
phase.set_time_options(fix_initial=True, duration_bounds=(.5, 10))

phase.add_state('x', fix_initial=True, fix_final=True)

phase.add_state('y', fix_initial=True, fix_final=True)

phase.add_state('v', fix_initial=True, fix_final=False)

phase.add_control('theta', continuity=True, rate_continuity=True,
                  units='deg', lower=0.01, upper=179.9)

phase.add_parameter('g', units='m/s**2', val=9.80665)

#
# Minimize time at the end of the phase
#
phase.add_objective('time', loc='final', scaler=10)

p.model.linear_solver = om.DirectSolver()

#
# Setup the Problem
#
p.setup()

# intial guesses for times and controls are important before running a sim
p['traj.phase0.t_initial'] = 0.0
p['traj.phase0.t_duration'] = 2.0
p.set_val('traj.phase0.controls:theta', phase.interp('theta', ys=[5, 100.5]))


# need this to set initial conditions for x,y,v so simulate starts from the right condition
p.set_val('traj.phase0.states:x', phase.interp('x', ys=[0, 10]))
p.set_val('traj.phase0.states:y', phase.interp('y', ys=[10, 5]))
p.set_val('traj.phase0.states:v', phase.interp('v', ys=[0, 9.9]))

# Run a simulation to get physically valid initial guesses
p.run_model()
exp_out = traj.simulate()

# extract the initial values out of the sim result
x_sim = exp_out.get_val('traj.phase0.timeseries.states:x')
y_sim = exp_out.get_val('traj.phase0.timeseries.states:y')
v_sim = exp_out.get_val('traj.phase0.timeseries.states:v')
t_sim = exp_out.get_val('traj.phase0.timeseries.time')

# need to manually reset the last value to the correct BCs for states where fix_final=True
# but the initial values will be correct because we set them above before sim
x_sim[-1] = 10
y_sim[-1] = 5



# set initial guesses based on initial sim into the model
# (Dymos will re-interpolate them onto the collocation grid)
# comment/uncomment these three lines to see the effect of the initial guess
p.set_val('traj.phase0.states:x', phase.interp('x', xs=t_sim, ys=x_sim))
p.set_val('traj.phase0.states:y', phase.interp('y', xs=t_sim, ys=y_sim))
p.set_val('traj.phase0.states:v', phase.interp('v', xs=t_sim, ys=v_sim))

#
# Solve for the optimal trajectory
#
dm.run_problem(p)

# Check the results
print(p.get_val('traj.phase0.timeseries.time')[-1])

Full total jacobian was computed 3 times, taking 0.019553 seconds.
Total jacobian shape: (40, 50)


Jacobian shape: (40, 50)  (19.95% nonzero)
FWD solves: 13   REV solves: 0
Total colors vs. total size: 13 vs 50  (74.0% improvement)

Sparsity computed using tolerance: 1e-25
Time to compute sparsity: 0.019553 sec.
Time to compute coloring: 0.028856 sec.
Memory to compute coloring: 0.000000 MB.
Optimization terminated successfully    (Exit mode 0)
            Current function value: [18.0161673]
            Iterations: 24
            Function evaluations: 24
            Gradient evaluations: 24
Optimization Complete
-----------------------------------
[1.80161673]

Full total jacobian was computed 3 times, taking 0.020724 seconds.
Total jacobian shape: (40, 50)


Jacobian shape: (40, 50)  (19.95% nonzero)
FWD solves: 13   REV solves: 0
Total colors vs. total size: 13 vs 50  (74.0% improvement)

Sparsity computed using tolerance: 1e-25
Time to compute sparsity: 0.020724 sec.
Time to compute coloring: 0.029557 sec.
Memory to compute coloring: 0.000000 MB.
Optimization terminated successfully    (Exit mode 0)
            Current function value: [18.01657396]
            Iterations: 11
            Function evaluations: 11
            Gradient evaluations: 11
Optimization Complete
-----------------------------------
[1.8016574]

Optimization terminated successfully    (Exit mode 0)
            Current function value: [18.01612053]
            Iterations: 39
            Function evaluations: 39
            Gradient evaluations: 39
Optimization Complete
-----------------------------------
[1.80161205]

Optimization terminated successfully    (Exit mode 0)
            Current function value: [18.01677452]
            Iterations: 43
            Function evaluations: 44
            Gradient evaluations: 43
Optimization Complete
-----------------------------------
[1.80167745]